Modern day origin of the word “Ontology” comes from the world of metaphysics. It is a branch of metaphysics that deals with the nature of being. However, the word is ancient, with its early origins coming from a Greek philosphical concept. The word is gaining traction in the world of data management strategy these days. Common thread between philosophical ontology and data ontology is that both are an attempt to describe everything that is, i.e., entities, ideas, events, frameworks and all the relations between everything. 

At a high level, data ontology in data management describes the collection of critical enabling frameworks and technologies to tackle the wide gamut of data challenges experienced by organizations today. By this definition, frameworks like data mesh, data management approaches like data products, and technologies like vector databases are part of the data ontology landscape.

Just like every philosophical terminology, there are various interpretations of ontology in the world of data management as well. In one of my recent reports, Generative AI in Supply Chain: Key Considerations, I leveraged this term with the suggestion that in order to effectively leverage the full value of Gen AI 8-10 years from now, you need to start incorporating your Gen AI roadmap in your ontology starting now.

For decades, we have been so engrossed with technical aspects of data, databases, and data management that we lost our ability to see the “forest and the trees” view. This led to the massive data and applications silos that the majority of organizations today struggle with. It resulted from the classic separation of art and science that is still so widely prevalent in the world of technology.

On the other hand, philosophy has been about making sense of everything and integrating everything together to make sense of the world. And for that, philosophers try to link everything together by defining what is meaningful. That same approach applies to data ontology as well.

Data ontology provides the map linking data and meaning by defining what is meaningful. These meaningful things are an organization’s nouns, verbs, and adjectives. For example, for a financial services company, the primary entities or classes of objects that it will be concerned with, are accounts, transactions, financial products etc. 

For each of these object classes, the organization would define object class definitions in an ontology, along with other concepts. These concepts will be connected in a web of defined relationships. This web provides a meaning to why data exists today the way it does and why it needs to evolve in specific ways to meet the challenges of tomorrow. For each object class, you can (or should) define specific properties that describe that class. 

Technical mumbo jumbo aside, the gist is that if you want to be an organization that is leading when it comes to leveraging GenAI for transformation and innovation a decade from now, this is a critical foundational capability.